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Data Mining and Knowledge Discovery Via Logic-Based Methods

Data Mining and Knowledge Discovery Via Logic-Based Methods


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About the Book

Algorithmic Issues.- Inferring a Boolean Function from Positive and Negative Examples.- A Revised Branch-and-Bound Approach for Inferring a Boolean Function from Examples.- Some Fast Heuristics for Inferring a Boolean Function from Examples.- An Approach to Guided Learning of Boolean Functions.- An Incremental Learning Algorithm for Inferring Boolean Functions.- A Duality Relationship Between Boolean Functions in CNF and DNF Derivable from the Same Training Examples.- The Rejectability Graph of Two Sets of Examples.- Application Issues.- The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosis.- Data Mining and Knowledge Discovery by Means of Monotone Boolean Functions.- Some Application Issues of Monotone Boolean Functions.- Mining of Association Rules.- Data Mining of Text Documents.- First Case Study: Predicting Muscle Fatigue from EMG Signals.- Second Case Study: Inference of Diagnostic Rules for Breast Cancer.- A Fuzzy Logic Approach to Attribute Formalization: Analysis of Lobulation for Breast Cancer Diagnosis.- Conclusions.


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Product Details
  • ISBN-13: 9781461426134
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Edition: 2010 ed.
  • Language: English
  • Returnable: Y
  • Spine Width: 23 mm
  • Weight: 589 gr
  • ISBN-10: 1461426138
  • Publisher Date: 05 Sep 2012
  • Binding: Paperback
  • Height: 229 mm
  • No of Pages: 350
  • Series Title: Springer Optimization and Its Applications
  • Sub Title: Theory, Algorithms, and Applications
  • Width: 155 mm


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